Check health professions papers: citations, claims and retractions

Assessing reading levels of health information: uses and limitations of flesch formula

Jindal, P., & MacDermid, J. (2017) · Education for Health

Health Literacy and Information AccessibilityText Readability and Simplification

Cited by 275

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers

Maniruzzaman, M., Rahman, M. J., Al-MehediHasan, M., Suri, H. S., Abedin, M. M., El-Baz, A., & Suri, J. S. (2018) · Journal of Medical Systems

Artificial Intelligence in HealthcareData Mining and Machine Learning Applications

Cited by 275

Predicting the Adoption of Electronic Health Records by Physicians: When Will Health Care be Paperless?

Ford, E. W., Menachemi, N., & Phillips, M. T. (2006) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsHealthcare Policy and Management

Cited by 275

Adoption and non-adoption of a shared electronic summary record in England: a mixed-method case study

Greenhalgh, T., Stramer, K., Bratan, T., Byrne, E., Russell, J., & Potts, H. W. W. (2010) · BMJ

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 275

Security Techniques for the Electronic Health Records

Kruse, C. S., Smith, B., Vanderlinden, H., & Nealand, A. (2017) · Journal of Medical Systems

Electronic Health Records SystemsThoreau and American Literature

Cited by 275

RxNorm: prescription for electronic drug information exchange

Liu, S., Ma, W., Moore, R., Ganesan, V., & Nelson, S. (2005) · IT Professional

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 274

Electronic health records-driven phenotyping: challenges, recent advances, and perspectives

Pathak, J., Kho, A. N., & Denny, J. C. (2013) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 274

The effect of social support features and gamification on a Web-based intervention for rheumatoid arthritis patients: randomized controlled trial

Allam, A., Kostova, Z., Nakamoto, K., & Schulz, P. J. (2015) · Journal of Medical Internet Research

Mobile Health and mHealth ApplicationsEducational Games and Gamification

Cited by 274

Catastrophizing and internal pain control as mediators of outcome in the multidisciplinary treatment of chronic low back pain

Spinhoven, P., ter Kuile, M., Kole‐Snijders, A. M. J., Mansfeld, M. H., den Ouden, D., & Vlaeyen, J. W. S. (2004) · European Journal of Pain

Musculoskeletal pain and rehabilitationPain Management and Treatment

Cited by 274

Correlates of physician burnout across regions and specialties: a meta-analysis

Lee, R. T., Seo, B., Hladkyj, S., Lovell, B. L., & Schwartzmann, L. (2013) · Human Resources for Health

Healthcare professionals’ stress and burnoutOccupational Health and Burnout

Cited by 274

Health Risks of Flood Disasters

Paterson, D. L., Wright, H., & Harris, P. N. A. (2018) · Clinical Infectious Diseases

Disaster Response and ManagementClimate Change and Health Impacts

Cited by 274

The Effectiveness of Prompts to Promote Engagement With Digital Interventions: A Systematic Review

Alkhaldi, G., Hamilton, F. L., Lau, R., Webster, R., Michie, S., & Murray, E. (2016) · Journal of Medical Internet Research

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 273

Impact of remote patient monitoring on clinical outcomes: An updated meta-analysis of randomized controlled trials

Noah, B., Keller, M. S., Mosadeghi, S., Stein, L., Johl, S., Delshad, S., Tashjian, V. C., Lew, D., Kwan, J. T., Jusufagic, A., et al. (2018) · npj Digital Medicine

Mobile Health and mHealth ApplicationsTelemedicine and Telehealth Implementation

Cited by 273

HIPAA and protecting health information in the 21st century

Cohen, I. G., & Mello, M. M. (2018) · JAMA

Thoreau and American LiteratureElectronic Health Records Systems

Cited by 273

Trial to Examine Text Message–Based mHealth in Emergency Department Patients With Diabetes (TExT-MED): A Randomized Controlled Trial

Arora, S., Peters, A. L., Burner, E., Lam, C. N., & Menchine, M. (2014) · Annals of Emergency Medicine

Mobile Health and mHealth ApplicationsDiabetes Management and Education

Cited by 273

A randomised controlled trial of Tai Chi and resistance exercise on bone health, muscle strength and balance in community-living elderly people

Woo, J., Hong, A., Lau, E., & Lynn, H. (2007) · Age and Ageing

Balance, Gait, and Falls PreventionBone health and osteoporosis research

Cited by 273

Comparison of energy and protein intakes of older people consuming a texture modified diet with a normal hospital diet

Wright, L., Cotter, D., Hickson, M., & Frost, G. (2005) · Journal of Human Nutrition and Dietetics

Dysphagia Assessment and ManagementNutrition and Health in Aging

Cited by 273

Harnessing the cloud of patient experience: using social media to detect poor quality healthcare

Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L. (2013) · BMJ Quality & Safety

Patient Satisfaction in HealthcareSentiment Analysis and Opinion Mining

Cited by 272

Assistive Technology Use and Stigma

Parette, P., & Scherer, M. (2004) · Education and Training in Developmental Disabilities

Assistive Technology in Communication and MobilityInclusion and Disability in Education and Sport

Cited by 272

Effect of Pain Neuroscience Education Combined With Cognition-Targeted Motor Control Training on Chronic Spinal Pain: A Randomized Clinical Trial

Malfliet, A., Kregel, J., Coppieters, I., De Pauw, R., Meeus, M., Roussel, N., Cagnie, B., Danneels, L., & Nijs, J. (2018) · JAMA Neurology

Musculoskeletal pain and rehabilitationPain Management and Treatment

Cited by 272

Tailoring quality improvement interventions to identified barriers: a multiple case analysis

Bosch, M., Van Der Weijden, T., Wensing, M., & Grol, R. (2007) · Journal of Evaluation in Clinical Practice

Health Policy Implementation ScienceHealthcare Quality and Management

Cited by 272

Balance, gait, and falls in multiple sclerosis

Cameron, M. H., & Nilsagård, Y. E. (2018) · Handbook of Clinical Neurology

Balance, Gait, and Falls PreventionMultiple Sclerosis Research Studies

Cited by 271

Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants

Qi, X., Wang, S., Fang, C., Jia, J., Lin, L., & Yuan, T. (2025) · Redox Biology

Artificial Intelligence in HealthcareNutritional Studies and Diet

Cited by 271

How important are back pain beliefs and expectations for satisfactory recovery from back pain?

Main, C. J., Foster, N., & Buchbinder, R. (2010) · Best Practice & Research Clinical Rheumatology

Musculoskeletal pain and rehabilitationPain Management and Placebo Effect

Cited by 271